System and method of analyzing user engagement activity in social media campaigns

ABSTRACT

A server-based system is provided for analyzing and valuating social media campaign performance. The system collects online traffic data from member users of a social media channel, and tracks engagement activity with respect to a social media campaign operating on the channel according to parameters defined with respect to specific activities including: user interactions with media content of the campaign; user transactions as non-commercial data exchanges or commercial purchases conducted via the campaign; and user sharing of the campaign with other member users of the social media channel. The system establishes baseline performance thresholds and aggregates the tracked engagement activity with respect to the defined parameters. A graphical user interface is generated to display client feedback related to a comparison of the aggregated engagement activity with the established baseline performance thresholds for each of the defined parameters.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/955,680, filed Jul. 31, 2013; which is a continuation of U.S. patentapplication Ser. No. 13/616,720 (now U.S. Pat. No. 8,504,616), filedSep. 14, 2012; which is a continuation of U.S. patent application Ser.No. 13/288,447 (now U.S. Pat. No. 8,291,016), filed Nov. 3, 2011; andfurther claims benefit of U.S. Provisional Patent Application No.61/528,809, filed Aug. 30, 2011, which is hereby incorporated byreference.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the reproduction of the patent document or the patentdisclosure, as it appears in the U.S. Patent and Trademark Office patentfile or records, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

The present invention relates generally to a system and associatedmethods for analyzing and valuating social media campaigns. Moreparticularly, this invention relates to a system and associated methodsfor monitoring various aspects of user engagement with respect to asocial media campaign.

As social networking has evolved in recent years, business entities haveincreasingly broadened their definitions of online advertising toinclude social media campaigns. Such campaigns are typically designed toextend far beyond the conventional banner ads, and may instead seek toprovide a creative experience which is customized to generateword-of-mouth throughout target demographics or otherwise utilizeinteractive, motivating and “community-building” features which areinherent to social media.

However, the use of social commerce analytics to make such social mediacampaigns function properly or otherwise maximize their benefit isrelatively new. As a result, companies struggle to properly monetizetheir campaigns, whether in making initial decisions on how to allocatetheir resources, or in gauging the strength and success of theircampaigns and making corrective actions to ensure optimal futurecampaign performance.

For example, a company or merchant may find it particularly advantageousto integrate with social media channels that generate the appropriatelevel and type of feedback for its products, services, values, etc.Identifying a target online demographic may require not only identifyinga target consumer for your particular goods and services and ademographic associated with a particular social media channel, butobserving the interaction of visitors over time and tracking associatedperformance variables to valuate individual campaigns. Most currentanalytical platforms either provide direct feedback as to a number ofviews, links, orders, recommendations, etc., or provide indirectfeedback as to the ability of an advertisement to generate traffic onthe commercial site, without substantive analysis of (for example) thecampaign's impact relative to other campaigns or across a number ofchannels.

It would therefore be desirable to provide a social commerce analyticsplatform capable of generating feedback for a user that includes visitorscoring across a spectrum of parameters customized to the particularclient/merchant, or otherwise selected and modified over time tomaximize the benefit of associated social media campaigns to the client.

BRIEF SUMMARY OF THE INVENTION

Briefly stated, a host system in accordance with the present disclosureutilizes social commerce analytics and aggregates the data from socialnetworks around a single profile or page, such as friends, followers,“likes”, “comments” and “shares” as well as the transactional data fromclient storefronts (connected to that same profile or page), such ascart conversion, best selling products and total revenue. The aggregateddata is used to present connections between social networking activityand e-commerce transactions, to the owner or authorized manager of theprofile or page, as well as to visualize the value of the profile orpage's social graph in a fan value spectrum.

Various algorithms may be used to calculate key metrics such as forexample but without limitation an average amount of revenue per fan, fanstatus by revenue, and/or value of social graph.

In one aspect of the present disclosure, the host system tracks allvisitors, anonymously, from impressions of a social media campaign(e.g., storefronts) associated with the online platform (i.e., socialmedia channel) through the use of data collection media as previousknown in the art, such as for example cookies.

In another aspect, the host system captures engagement activities fromthose anonymously tracked visitors such as for example ‘listened tosong’, ‘watched video’, ‘clicked link.’

In another aspect, the host system connects the data collected fromanonymous visitor tracking to authorized registered accounts through thehost platform.

In another aspect, the host system measures the conversion funnel fromanonymous impression through transaction.

In another aspect, the host system integrates with social networks suchas for example Facebook, Twitter and MySpace to connect accountsregistered with the host platform with accounts that “like” or “follow”brands when proper permissions have been granted to the host system bythe account holder.

In another aspect, the host system creates a spectrum of fan engagement(“like” to “buy”) with quantity and value with a unique combination ofdata which may include but is not limited to anonymous visitor tracking,host account tracking, and data from Social Network graphs on hostaccount holders.

In one embodiment, a hosted server-based system as disclosed hereinanalyzes and valuates social media campaign performance by collectingonline traffic data from member users of a social media channel, andtracking engagement activity with respect to a social media campaignoperating on the channel according to parameters defined with respect tospecific activities including: user interactions with media content ofthe campaign; user transactions as non-commercial data exchanges orcommercial purchases conducted via the campaign; and user sharing of thecampaign with other member users of the social media channel. The systemestablishes baseline performance thresholds and aggregates the trackedengagement activity with respect to the defined parameters. A graphicaluser interface is generated to display client feedback related to acomparison of the aggregated engagement activity with the establishedbaseline performance thresholds for each of the defined parameters.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram representing an embodiment of a hosted systemas further described herein.

FIG. 2 is a block diagram representing another embodiment of a hostedsystem as further described herein.

FIG. 3 is a flowchart representing an exemplary method as may beexecuted by a hosted system as further described herein.

FIG. 4 is a flowchart representing an exemplary conversion process fromanonymous visitor to linked account to transaction via a social mediacampaign as further described herein.

FIG. 5 is a flowchart representing an exemplary analytics data flow asfurther described herein.

DETAILED DESCRIPTION OF THE INVENTION

Throughout the specification and claims, the following terms take atleast the meanings explicitly associated herein, unless the contextdictates otherwise. The meanings identified below do not necessarilylimit the terms, but merely provide illustrative examples for the terms.The meaning of “a,” “an,” and “the” may include plural references, andthe meaning of “in” may include “in” and “on.” The phrase “in oneembodiment,” as used herein does not necessarily refer to the sameembodiment, although it may.

Terms such as “providing,” “processing,” “supplying,” “determining,”“calculating” or the like may refer at least to an action of a computersystem, computer program, signal processor, logic or alternative analogor digital electronic device that may be transformative of signalsrepresented as physical quantities, whether automatically or manuallyinitiated.

Referring generally to FIGS. 1-5, various systems and methods aredescribed herein for providing social commerce analytics andoptimization. Generally stated, the systems and methods seek to obtaindata from a plurality of online sources and deploy social commerceanalytics solutions to identify or otherwise report commercialopportunities to an e-commerce platform manager with respect toassociated social media campaigns. Where the various figures maydescribe embodiments sharing various common elements and features withother embodiments, similar elements and features are given the samereference numerals and redundant description thereof may be omittedbelow.

Referring first to FIG. 1, in one embodiment a social commerce analyticssystem 10 is embodied by or within either of a central hosted server 12or a plurality of servers 12 functionally linked and collectivelydefining a distributed host network (not shown). The server 12 asrepresented in FIG. 1 further includes a processor 14, acomputer-readable memory medium 16, a database 20 and an I/O platform ormodule 22 which may typically include a user interface generated by theprogram instructions in accordance with methods or steps described ingreater detail below.

The term “computer-readable memory medium” as used herein may refer toany non-transitory medium 16 alone or as one of a plurality ofnon-transitory memory media 16 within which is embodied a computerprogram product 18 that includes processor-executable software,instructions or program modules which upon execution may provide data orotherwise cause a computer system to implement subject matter orotherwise operate in a specific manner as further defined herein. It mayfurther be understood that more than one type of memory media may beused in combination to conduct processor-executable software,instructions or program modules from a first memory medium upon whichthe software, instructions or program modules initially reside to aprocessor for execution.

“Memory media” as generally used herein may further include withoutlimitation transmission media and/or storage media. “Storage media” mayrefer in an equivalent manner to volatile and non-volatile, removableand non-removable media, including at least dynamic memory, applicationspecific integrated circuits (ASIC), chip memory devices, optical ormagnetic disk memory devices, flash memory devices, or any other mediumwhich may be used to stored data in a processor-accessible manner, andmay unless otherwise stated either reside on a single computing platformor be distributed across a plurality of such platforms. “Transmissionmedia” may include any tangible media effective to permitprocessor-executable software, instructions or program modules residingon the media to be read and executed by a processor, including withoutlimitation wire, cable, fiber-optic and wireless media such as is knownin the art.

The host server 12 may be accessible by or otherwise linked to variousexternal and third party platforms via a communications network 24 andin accordance with these links is effective to collect, obtain, receive,transmit and/or share data using various communications protocols as arewell known in the art. The term “communications network” 24 as usedherein with respect to data communication between two or more parties orotherwise between communications network interfaces associated with twoor more parties may refer to any one of, or a combination of any two ormore of, telecommunications networks (whether wired, wireless, cellularor the like), a global network such as the Internet, local networks,network links, Internet Service Providers (ISP's), and intermediatecommunication interfaces as are known in the art.

The term “platform” as used herein may typically refer to a website,network, or alternative but equivalent interfaces for supportingmultimedia interactivity consistent with the described features of thepresent disclosure.

A “client” as referred to herein may refer to a merchant, e-commercesite manager, administrator or otherwise interested entity on whosebehalf the systems and methods of the present disclosure perform thesocial commerce analytics. Access by the client to the results of thesystems and methods described herein may be provided through a dashboardand various screens of data and graphs in accordance with securitymeasures such as user login and passwords or other equivalent means asare well known in the art and otherwise not described herein.

The term “online traffic data” as used herein may unless otherwisestated be understood in accordance with its plain meaning to those ofskill in the art, but may generally for the purposes of the systems andmethods of the present disclosure be centered on information regardingconsumers or potential consumers of the client platforms and associatedstorefronts. One example of such data may include web traffic datacollected from web servers, third party analytics systems and IPintelligence systems, and which can provide geographic information,information about the user making the web request, demographicinformation, session length, and other distinguishable data regardingthe web request. Another example of such data may be that connected toan email address, or the encrypted hash of an email address, from athird party data provider or collected directly through the host system,that provides personally identifying information, transactional data,demographic data, interest data and other data uniquely connected to theemail address. Another example may include data collected from socialnetworks that employ a social graph, and enable, by permission, the dataof that social graph to be collected on a member of the social network.Yet another example may be data collected from e-commerce transactionsmade through the host system.

Referring again to FIG. 1, examples of platforms which are typicallylinked to the host server 12 in accordance with methods as disclosedherein may include social networking platforms 26, client platforms 30,and third party platforms 34. In a typical embodiment, one or more ofthe social networking platforms 26 may generally include or provideaccess to a social media campaign 28 associated with a client of thesystem 10, and the host server 12 may be programmed to collect, receiveor otherwise obtain information from the social networking platform 26regarding online traffic and/or related visitor activity with respect tothe social media campaign 28 or even more generally with regards to theplatform 26 for the purposes of comparison.

Likewise, one or more of the client platforms 30 may generally include astorefront 32 associated with the client managing or otherwise operatingthe campaigns described above, and the host server 12 may be programmedto collect, receive or otherwise obtain information from the clientplatform 30 regarding online traffic and/or related visitor activitywith respect to the storefront 32 or even more generally with regards tothe platform 30 for the purposes of comparison.

The term “storefront” as used herein may typically refer to an interfacegenerated in association with the underlying platform which provides foror otherwise facilitates the selection, purchase, transfer, etc., ofgoods or services offered by the client. In various embodiments theclient platform 30 may be associated with a client entity that does notcommercially offer goods or services, wherein the storefront 32 moregenerally refers to an interface or portal for interacting withvisitors, obtaining feedback, soliciting donations, or the like.

In certain embodiments a third party platform 34 having an associatedportal or storefront 36 may be further linked to the host server 12 orotherwise integrated with the system 10 for the purpose of providingcomparison data, for example with respect to the online traffic on thesocial networking platforms or with respect to the commercial traffic onthe storefronts associated with the client. In some cases, the thirdparty platform 34 with respect to one client may in fact be a secondclient of the system 10 wherein the first client maintains one or morethird party platforms 34 with respect to the second.

Referring now to FIG. 2, in another embodiment the client platform 30and the host server 12 of the system 10 may be the same entity. Thesocial networking platforms 26 and third party platforms 34 are stilllinked to the server 12 and to the storefronts 32 via the communicationsnetwork 24, but it may be understood that certain steps would beredundant in such configurations, including but not limited to the needto transmit and receive data between the client platforms and the hostserver of FIG. 1.

Further within the scope of the present disclosure, it may beanticipated that the host server 12 in association with one clientplatform 30 (as represented in FIG. 2) may further function to performmethods for or on behalf of other client platforms 30 (as represented inFIG. 1).

Referring now to FIG. 3, a social commerce analytics method 100 may nowbe described in accordance with an embodiment of a system according tothe present disclosure. For illustrative purposes, the various steps ofthe method 100 may refer to the system 10 as represented in FIG. 1, butcertain steps may be considered as redundant in view of alternativesystem configurations or even optional unless otherwise stated. Further,the steps of the method 100 as represented in FIG. 3 are not intended aslimiting on the actual chronology of their performance, unless otherwisestated or logically required.

As a first step (102) of the method 100 or alternatively as a precursorto the steps as executed by a host system 10 as disclosed herein, onlineactivity is conducted by a plurality of visitors and coincidently orsubsequently monitored or recorded with respect to one or more socialnetworking platforms (102 a), client platforms (102 b) and third partyplatforms (102 c).

The online traffic data from the respective social networking platformswhich is relevant to the method 100 may include without limitationgeneral online traffic data, activity data (user interaction) withrespect to one or more social media campaigns related to the client andoperating via the social networking platform, activity data with respectto other campaigns which may be relevant or otherwise relate to theclient campaign, activity data by consumers associated with the clientplatform or other visitors determined to be associated with suchconsumers, and the like. Examples of data gathering may include activesteps on the part of the host system, such as for example via the use ofcode snippets, cookies or other data collection media operating withinthe social media campaign interface or elsewhere within the socialnetworking platform, and may involve for example anonymous visitortracking, data scraping, etc. Alternatively, examples of data gatheringmay be more passive, such as for example and without limitation bydirectly requesting data from the visitor, requesting data from theplatform administrator, obtaining permission to review or download allor portions of a social graph associated with an individual visitor orrelated member spectrum data associated with the platform generally. Thesystem may accordingly track visitor engagement actions across thevarious platforms including for example but without limitation theplaying of video or audio media associated with the campaigns, “sharing”a campaign or storefront with other users of the platform, etc.

The online traffic data from the respective client (typically merchantor otherwise e-commerce) platforms which is relevant to the method 100may include without limitation general online traffic data, and activitydata (e.g., user/consumer interaction) with respect to one or morestorefronts related to the client and operating via the client platformas derived from individual visitors or groups of visitors, and which maybe broken down further with respect to consumers, visitors who fail tomake a purchase, repeat visitors who fail to make purchases, repeatconsumers, “friends” or the like with respect to prior consumers, etc.Other online traffic data of typical relevance to the method 100includes transactional details specific to a visitor such as for exampleadding products or services to a virtual shopping cart for purchase,dates and amounts of purchases, means for discovering or otherwisenavigating to the storefront, etc., or general storefront data regardingoverall revenues, traffic volume, revenues per data point (e.g., pervisitor, per day, per product sold, etc.) and the like.

The online traffic data from the respective third party platforms whichis relevant to the method 100 may typically be similar in content tothat of the client platforms, as their primary importance to the methodis for direct comparison in steps which will be further described below.

The online traffic data and other relevant data as described or alludedto above are collected or otherwise obtained and subsequently stored inthe server for use by the system algorithms or for basic reporting andcomparison purposes (step 104). Data may be remotely sourced from forexample electronic files, databases, as streamed in substantiallyreal-time, etc., or may be received at the host server via bundled datatransmissions from external parties (e.g., clients, social networkingadministrators, third party contributors, online visitors, members ofthe host system, etc.). An exemplary analytics data flow is furtherrepresented in FIG. 5, wherein data is obtained electronically bypermission, aggregated and analyzed, etc., as further described inaccordance with the method 100.

One such exemplary system algorithm may be used (step 106) to establisha baseline performance standard for more generally measuring orcomparing actual future commercial/transactional performance withrespect to the storefronts/platforms. In various embodiments, a varietyof for example organic, third-party, raw, processed data sources maywithout limitation be leveraged by system algorithms to construct asuite of baseline parameters and methods which either alone orcollectively may define a baseline scoring system. The number and typesof parameters defining a scoring baseline may grow over time, and may becombined in any order as needed to construct thresholds which may, butnot necessarily, be customized post facto based on the candidateperformance data being evaluated. Exemplary parameters may be withoutlimitation temporal, categorical, single integers, lists of integers,real numbers, lists of real numbers, categorical lists, probabilities,etc., and may relate to any type or number of relevant data points,commercial or otherwise, as desired by a particular client.

The generation of parameters for inclusion into the baseline scoringsystem may occur based on information defined by (for example) ananalytics manager (using predetermined criteria and stored historicaldata), a client user of the system (user-defined criteria), patternsconstructed over time through machine learning (determined andperformance-defined criteria), or some combination of the same. Someweighting in parameter generation may further evolve over time, wherefor example user-defined criteria may be more heavily relied uponinitially, but is gradually reduced in weight relative toperformance-defined criteria as the algorithm may more reliably base theparameters on a broader spectrum of relevant input data.

Baseline performance thresholds may be established by systems,algorithms, program modules, etc., of the present disclosure withrespect to individual campaigns, channels and/or storefronts, oraggregations of any or all of the same, further with respect to forexample commercial performance, traffic performance, and other relatedand derived data points. In an exemplary process, a particular clientmay set up multiple storefronts, each containing multiple products,which operate within or are otherwise linked to a number of campaignsthat are offered into various social (or non-social) media channels.Baseline thresholds may be set up for each of the campaigns (relating tofor example raw online traffic, levels of interactivity, conversionpercentages with respect to the linked storefronts), each of thestorefronts (relating to for example revenue, purchases relative to thenumber of visitors, raw online traffic), individual products, or anaggregate of the above which parameters may be defined largely by thenumber of campaigns or storefronts being provided in the first place.The particular campaigns and storefronts may vary in their individualbaseline parameters with respect to for example the number of expectedonline visitors or prior patterns in translating online traffic toactual revenue.

System algorithms and methods as disclosed herein may further generateactual performance scores for comparison against the baseline parameters(step 108) by identifying and scoring/valuating visitors based on anumber of scoring metrics.

In an embodiment, visitors may be identified and labeled according to a“visitor engagement spectrum” or “fan engagement spectrum” designedaround or otherwise based upon specific activities tracked across all ora respective portion of the collected/aggregated dataset, and algorithmsare utilized to map the collected/obtained data to the fan spectrum foreach associated channel, brand, etc. In one particular example, thespectrum may evaluate “fans” and/or followers of a social networkingpage (campaign) into a number of defined sections such as the following:

A “Super Fan” may be designated as a member of the social network who isa fan of the campaign or storefront and has purchased multiple timesfrom the storefront, or has purchased once from the storefront and hasshared an offer to another member of the social network who hassubsequently made a purchase from the storefront.

A “Purchasing Fan” may be designated as a member of the social networkwho has purchased from the storefront at any time in the past.

An “Advocate Fan” may be a member of the social network who has sharedan offer to another member of the social network who subsequently made apurchase from the storefront.

An “Engaged Fan” may be a member of the social network who is a fan ofthe campaign or storefront and has shared an offer to another member ofthe social network.

A “Fan” may be a member of the social network who is a fan of thecampaign or storefront generally.

A “Potential Fan” may be a member of the social network who has onedegree of separation from the campaign or storefront.

FIG. 4 represents an exemplary identification process, wherein aninitially anonymous visitor is typically redefined or reevaluated in thefan spectrum at the bottom of the diagram after providing permission tothe host system to access social graph data, and subsequently engagingand conducting transactions via the commercial storefront.

Returning to the scoring/valuation portion of step 108, through aweighting system of the various data points, for example with theheaviest weight applied to transactions and the lightest weight appliedto anonymous traffic, and/or with the weight impacted by recentness(e.g., the most recent purchaser gets a higher weight than the leastrecent purchaser), the system may thereby give a score to the visitorwith respect to all other visitors. The score can be applied to all ofthe people being measured in a client's account on the host system, or asubset (down to one person).

Data may be measured from any one of a plurality of perspectives inaccordance with various embodiments of a system and method as disclosedherein. For example, the data may be measured according to the “channel”or location where the web traffic occurred, where the email address wascollected, where the social graph data was collected, or where thetransaction took place. Data may be measured in accordance with a personor a collection of people grouped by common metadata. Data may furtherbe measured according to an offer made through the system to the peoplein an effort to collect an email address or social graph permissions ora transaction. Data may further be aggregated together in any and allcombinations including sources listed above as well as various otherswithout limitation and within the scope of the present disclosure, tomeasure other data and/or be measured by other data.

An exemplary scoring process may be described as follows. The existenceof traffic data gets a base score of 10. The existence of email addressdata gets a base score of 30. The existence (and permission to collector obtain) of social graph data gets a base score of 60. The existenceof transactional data gets a base score of 100.

A client accesses the system to review activity as collected, collated,determined, scored, etc., by the host system on their behalf. Uponlooking at the view of a particular offer, they see that a person viewedthe offer through Facebook. Facebook in this particular example may havebeen applied a channel weight of 2× the normal (perhaps becausehistorically Facebook has generated twice as much activity for them asother channels). As a result, this person's traffic score is 20 (2×10).

The person did not (or could not) give permission regarding their socialgraph data, but they did make a purchase. For that, the person gets boththe email address base score and the transactional base score, equaling130 (30+100).

Finally, the exemplary transaction happened yesterday, so thetransactional base score is weighted upwards by 50% to 150 (100×1.5),bringing the total social commerce score for this user to 170 (20 fortraffic+30 for email+150 for transactions).

It may be understood that various alternative scoring metrics, weightingalgorithms, and valuation representations may be optimal or preferredacross a range of commercial applications, and the above process isintended as being exemplary only.

Having identified and scored the various individual visitors/fans, themethod may continue by aggregating valuations with respect to forexample respective social networking platforms, campaigns, storefronts,products, etc. (step 110).

In various embodiments, the system may in accordance with client-definedpreferences or parameters then provide valuation representations basedon the baseline parameters, actual performance, predicted performance,third party performance, or any combination of the same. As representedin FIG. 3 (step 112), the client may generally elect to displaycomparisons of the baseline parameters or third party performance datawith either of the actual performance or a predicted performance (orboth—not shown but as may be implied).

Where the client has selected actual performance comparisons, or wheresuch comparisons are even available (this would not be the caseinitially of course as the campaigns have not yet seen any onlinetraffic), the system may generate and subsequently display or otherwiseprovide access to comparisons of actual performance data with respect toany or all of the baseline performance parameters, historicalperformance data for the client, and third party performance data (step114). The actual performance data to be used for comparison may beautomatically selected in view of the available stored data or may beselectable by the client, and in certain embodiments in accordance withthe present disclosure an interface (e.g., dashboard) may be generatedby the system in accordance with predetermined client criteria andauto-populated to represent respective valuations and comparisons. Thesystem may indicate to or otherwise alert the client of areas ofunderperformance where such comparisons result in actual performancefalling below the relevant baseline performance thresholds (step 116).Such indications may include for example and without limitationpassively generating the determined result, may include color-coding,changing the order of presentation or otherwise highlighting the areasof underperformance, or may alternatively include a more proactiveapproach such as generating a pop-up screen or box when the client logsinto the system. In certain embodiments the system may be configured torank parameters with regards to their relative importance to theparticular e-commerce (or other) objectives of the client, those whichcan be excluded, etc. The system may in various alternative embodimentsor as selectable and supplemental options to the above examples scorethe comparisons by various other criteria for the purpose of suggestingimprovements in performance, albeit in a more passive manner. Giventhese under-performance alerts, the campaign manager (client) canrespond within the framework and take corrective action for the purposeof facilitating optimal campaign performance going forward.

Where the client has selected predicted performance comparisons, or inan initial condition where some actual comparisons are not available assome or all of the campaigns have not yet seen any online traffic, thesystem may generate predicted performance parameters based on initialsettings or modifications to the existing parameters such as for examplethe number of campaigns, number of storefronts, transferring one or morecampaigns to different channels, etc. (step 118). The system furthercompares the predicted performance data to any or all of the baselineperformance parameters, actual performance data, third party performancedata, etc. (step 120), and then may (in step 122) suggest or otherwiserecommend modifications or courses of action to increase performance inrelation to any or all parameters with the ultimate goal of for exampleoptimizing performance within the distributed social commerceenvironment.

In certain embodiments two or more of the above steps may be combinedinto a single step where for example a program module is executed tointernally generate one or more virtual modifications, test predictedresults of the virtual modifications, and subsequently suggest any oneor more virtual modifications which are determined by the system togenerate potential improvements in performance with respect to currentactual performance, third party performance and/or baseline parameters.

Alternatively, the system may include a step (not shown) of receivingone or more potential modifications and a request from the client topredict their performance against the current actual performance, thirdparty performance, and/or baseline parameters, at which time the systemmay be programmed or configured to generate, test and score predictedresults for the provided modifications accordingly.

The previous detailed description has been provided for the purposes ofillustration and description. Thus, although there have been describedparticular embodiments of an invention as disclosed herein, it is notintended that such references be construed as limitations upon the scopeof this invention except as set forth in the following claims.

What is claimed is:
 1. A server system comprising a non-transitorycomputer-readable medium, the computer-readable medium furthercomprising program instructions executable by a processor to direct theperformance of: collecting online traffic data from member users of asocial media channel via a communications network, the social mediachannel comprising a social media campaign associated with a client;tracking engagement activity by said member users with respect to thesocial media campaign, the engagement activity tracked according to oneor more parameters defined with respect to each of a plurality ofspecific activities comprising user interactions with media content ofthe social media campaign, user transactions as non-commercial dataexchanges or commercial purchases conducted via the social mediacampaign, and user sharing of the social media campaign with othermember users of the social media channel; establishing baselineperformance thresholds with respect to each of the plurality ofparameters; aggregating the tracked engagement activity with respect tothe defined parameters; and generating a user interface for a displayunit associated with the client, the interface comprising clientfeedback related to a comparison of the aggregated engagement activitywith the established baseline performance thresholds for each of thedefined parameters.
 2. The server system of claim 1, the programinstructions further executable to direct the performance of identifyingand labeling individual users according to one or more portions of auser engagement spectrum based upon previously collected online trafficdata with respect to the client.
 3. The server system of claim 2, theprogram instructions further executable to direct the performance ofidentifying current users and potential users of the social mediacampaign from among the plurality of member users of the social mediachannel, wherein one or more parameters are further defined with respectto user impressions by the social media campaign on current users andpotential users.
 4. The server system of claim 2, wherein one or moreparameters for user interactions with the social media campaign aredefined in accordance with the user engagement spectrum.
 5. The serversystem of claim 2, wherein one or more parameters for user transactionsvia the social media campaign are defined in accordance with the userengagement spectrum.
 6. The server system of claim 5, wherein usertransaction activities for an identified user are tracked and aggregatedwith respect to one or more previous interactions, previoustransactions, and interactions without completing a transaction.
 7. Theserver system of claim 2, wherein one or more parameters for usersharing of the social media campaign are defined in accordance with theuser engagement spectrum.
 8. A server system comprising a non-transitorycomputer-readable medium, the computer-readable medium furthercomprising program instructions executable by a processor to direct theperformance of: collecting online traffic data from member users of asocial media channel via a communications network, the social mediachannel comprising a social media campaign associated with a client;tracking engagement activity comprising member user interactions withmedia content of the social media campaign, member user transactions asnon-commercial data exchanges or commercial purchases conducted via thesocial media campaign, and member user sharing of the social mediacampaign with other member users of the social media channel; definingone or more parameters customized with respect to the client, each ofthe one or more parameters defined with respect to one or more of thetracked member user activities; establishing one or more baselineperformance thresholds with respect to each of the one or moreparameters; aggregating the tracked engagement activity with respect tothe defined one or more parameters; and generating a user interface fora display unit associated with the client, the interface comprisingclient feedback related to a comparison of the aggregated engagementactivity with the established one or more baseline performancethresholds for each of the defined one or more parameters.
 9. The serversystem of claim 8, wherein one or more of the steps of defining one ormore parameters customized with respect to the client and establishingone or more baseline performance thresholds with respect to each of theone or more parameters comprises enabling an initial user selection ofone or more parameters or one or more baseline performance thresholds.10. The server system of claim 9, wherein the one or more stepscomprising an initial user selection further comprise modifying theinitial one or more parameters or one or more baseline performancethresholds over time based on aggregated engagement activity.
 11. Theserver system of claim 8, the program instructions further executable todirect the performance of identifying and labeling individual usersaccording to one or more portions of a user engagement spectrum basedupon previously collected online traffic data with respect to theclient.
 12. The server system of claim 11, the program instructionsfurther executable to direct the performance of identifying currentusers and potential users of the social media campaign from among theplurality of member users of the social media channel, wherein one ormore parameters are further defined with respect to user impressions bythe social media campaign on current users and potential users.
 13. Theserver system of claim 11, wherein one or more parameters for userinteractions with the social media campaign are defined in accordancewith the user engagement spectrum.
 14. The server system of claim 11,wherein one or more parameters for user transactions via the socialmedia campaign are defined in accordance with the user engagementspectrum.
 15. The server system of claim 14, wherein user transactionactivities for an identified user are tracked and aggregated withrespect to one or more previous interactions, previous transactions, andinteractions without completing a transaction.
 16. The server system ofclaim 11, wherein one or more parameters for user sharing of the socialmedia campaign are defined in accordance with the user engagementspectrum.